{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 5 (Main Text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded: jcr_mutiny_conflict_data.dta  |  Rows=1,599,624  Cols=130\n",
      "Contemporaneous duration ME index: ['C(mduration_t_cat3)[T.1.0]', 'C(mduration_t_cat3)[T.2.0]', 'lmmilex_persol_t', 'years_since_coup', 'ucdp_civconf_t2', 'defense_target', 'capprop', 'jdem', 'majorpower', 'cold_war', 't_since', 't2_since', 't3_since']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rmuhi\\AppData\\Local\\Temp\\ipykernel_18300\\3923793601.py:117: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  data['mutiny_t_lag1'] = data.groupby('id')['mutiny_t'].shift(1)\n",
      "C:\\Users\\rmuhi\\AppData\\Local\\Temp\\ipykernel_18300\\3923793601.py:118: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  data['violent_t_lag1'] = data.groupby('id')['violent_t'].shift(1)\n",
      "C:\\Users\\rmuhi\\AppData\\Local\\Temp\\ipykernel_18300\\3923793601.py:119: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  data['size_t_b_lag1'] = data.groupby('id')['size_t_b'].shift(1)\n",
      "C:\\Users\\rmuhi\\AppData\\Local\\Temp\\ipykernel_18300\\3923793601.py:120: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  data['mduration_t_cat3_lag1'] = data.groupby('id')['mduration_t_cat3'].shift(1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lagged sample size: 1,314,720 rows\n",
      "Lagged duration ME index: ['C(mduration_t_cat3_lag1)[T.1.0]', 'C(mduration_t_cat3_lag1)[T.2.0]', 'lmmilex_persol_t', 'years_since_coup', 'ucdp_civconf_t2', 'defense_target', 'capprop', 'jdem', 'majorpower', 'cold_war', 't_since', 't2_since', 't3_since']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# -----------------------------------------\n",
    "# 0) Lightweight installer if packages are missing\n",
    "# -----------------------------------------\n",
    "try:\n",
    "    import pandas as _pd; import statsmodels as _sm; import matplotlib as _mpl\n",
    "except Exception:\n",
    "    import sys, subprocess\n",
    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-U\", \"pandas\", \"statsmodels\", \"matplotlib\", \"seaborn\"])\n",
    "\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Aesthetic style\n",
    "sns.set(style=\"whitegrid\", context=\"notebook\", font_scale=1.1)\n",
    "\n",
    "# -----------------------------------------\n",
    "# 1) Load data\n",
    "# -----------------------------------------\n",
    "file_path = Path(r\"C:\\Users\\rmuhi\\OneDrive\\Desktop\\Mutiny_Conflict Paper_All\\Replication File\\jcr_mutiny_conflict_data.dta\")\n",
    "if not file_path.exists():\n",
    "    raise FileNotFoundError(f\"Could not find .dta file at: {file_path}\")\n",
    "\n",
    "data = pd.read_stata(file_path)\n",
    "print(f\"Loaded: {file_path.name}  |  Rows={len(data):,}  Cols={len(data.columns)}\")\n",
    "\n",
    "# -----------------------------------------\n",
    "# Helper: extract marginal effects\n",
    "# -----------------------------------------\n",
    "def extract_effect(result, var_name, label):\n",
    "    me = result.get_margeff(at='mean', method='dydx').summary_frame()\n",
    "    row = me.loc[var_name]\n",
    "    return {\n",
    "        'Variable': label,\n",
    "        'MarginalEffect': row['dy/dx'],\n",
    "        'SE': row['Std. Err.']\n",
    "    }\n",
    "\n",
    "# =========================================\n",
    "# PART A: Contemporaneous mutiny variables\n",
    "# =========================================\n",
    "\n",
    "# 2) Define contemporaneous formulas\n",
    "formula1 = 'dispute2 ~ mutiny_t + lmmilex_persol_t + years_since_coup + ucdp_civconf_t2 + defense_target + capprop + jdem + majorpower + cold_war + t_since + t2_since + t3_since'\n",
    "formula2 = 'dispute2 ~ violent_t + lmmilex_persol_t + years_since_coup + ucdp_civconf_t2 + defense_target + capprop + jdem + majorpower + cold_war + t_since + t2_since + t3_since'\n",
    "formula3 = 'dispute2 ~ size_t_b + lmmilex_persol_t + years_since_coup + ucdp_civconf_t2 + defense_target + capprop + jdem + majorpower + cold_war + t_since + t2_since + t3_since'\n",
    "formula4 = 'dispute2 ~ C(mduration_t_cat3) + lmmilex_persol_t + years_since_coup + ucdp_civconf_t2 + defense_target + capprop + jdem + majorpower + cold_war + t_since + t2_since + t3_since'\n",
    "\n",
    "# 3) Fit contemporaneous models\n",
    "result1 = smf.logit(formula1, data).fit(disp=False, cov_type='HC1')\n",
    "result2 = smf.logit(formula2, data).fit(disp=False, cov_type='HC1')\n",
    "result3 = smf.logit(formula3, data).fit(disp=False, cov_type='HC1')\n",
    "result4 = smf.logit(formula4, data).fit(disp=False, cov_type='HC1')\n",
    "\n",
    "# Optional: check names for duration categories\n",
    "me4 = result4.get_margeff(at='mean', method='dydx').summary_frame()\n",
    "print(\"Contemporaneous duration ME index:\", me4.index.tolist())\n",
    "\n",
    "# 4) Collect contemporaneous marginal effects\n",
    "effects_current = [\n",
    "    extract_effect(result1, 'mutiny_t', 'Military Mutiny'),\n",
    "    extract_effect(result2, 'violent_t', 'Violent Mutiny'),\n",
    "    extract_effect(result3, 'size_t_b', 'Large Mutiny'),\n",
    "    extract_effect(result4, 'C(mduration_t_cat3)[T.1.0]', 'Short Mutiny'),\n",
    "    extract_effect(result4, 'C(mduration_t_cat3)[T.2.0]', 'Long Mutiny')\n",
    "]\n",
    "\n",
    "me_current = pd.DataFrame(effects_current)\n",
    "\n",
    "# 5) Plot contemporaneous marginal effects\n",
    "y_positions_current = [2.2, 1.6, 1.0, 0.4, -0.2]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ax.set_facecolor('#f5f5f5')\n",
    "\n",
    "ax.errorbar(\n",
    "    x=me_current['MarginalEffect'],\n",
    "    y=y_positions_current,\n",
    "    xerr=me_current['SE'],\n",
    "    fmt='o',\n",
    "    color='black',\n",
    "    ecolor='dimgray',\n",
    "    elinewidth=1.8,\n",
    "    capsize=4,\n",
    "    capthick=1.4,\n",
    "    markersize=6\n",
    ")\n",
    "\n",
    "ax.axvline(0, color='gray', linestyle='--', linewidth=1)\n",
    "ax.set_yticks(y_positions_current)\n",
    "ax.set_yticklabels(me_current['Variable'])\n",
    "ax.invert_yaxis()\n",
    "\n",
    "ax.set_xlabel(\"Marginal Effect on MID Initiation (Challenger)\", fontsize=11)\n",
    "ax.set_title(\"Mutiny Characteristics (t)\", fontsize=12)\n",
    "\n",
    "ax.set_ylim(-0.7, 2.7)\n",
    "ax.set_xlim(\n",
    "    me_current['MarginalEffect'].min() - 0.0007,\n",
    "    me_current['MarginalEffect'].max() + 0.0005\n",
    ")\n",
    "\n",
    "for spine in ['top', 'right', 'left', 'bottom']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "ax.grid(True, linestyle=':', color='gray', alpha=0.4)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# =========================================\n",
    "# PART B: Lagged mutiny variables (t–1)\n",
    "# =========================================\n",
    "\n",
    "# 6) Create lagged versions of main IVs\n",
    "data['mutiny_t_lag1'] = data.groupby('id')['mutiny_t'].shift(1)\n",
    "data['violent_t_lag1'] = data.groupby('id')['violent_t'].shift(1)\n",
    "data['size_t_b_lag1'] = data.groupby('id')['size_t_b'].shift(1)\n",
    "data['mduration_t_cat3_lag1'] = data.groupby('id')['mduration_t_cat3'].shift(1)\n",
    "\n",
    "# 7) Drop rows with missing values due to lagging\n",
    "data_lagged = data.dropna(subset=[\n",
    "    'mutiny_t_lag1', 'violent_t_lag1', 'size_t_b_lag1', 'mduration_t_cat3_lag1',\n",
    "    'lmmilex_persol_t', 'years_since_coup', 'ucdp_civconf_t2',\n",
    "    'defense_target', 'capprop', 'jdem', 'majorpower', 'cold_war',\n",
    "    't_since', 't2_since', 't3_since', 'dispute2'\n",
    "])\n",
    "\n",
    "print(f\"Lagged sample size: {len(data_lagged):,} rows\")\n",
    "\n",
    "# 8) Lagged formulas\n",
    "formula5 = 'dispute2 ~ mutiny_t_lag1 + lmmilex_persol_t + years_since_coup + ucdp_civconf_t2 + defense_target + capprop + jdem + majorpower + cold_war + t_since + t2_since + t3_since'\n",
    "formula6 = 'dispute2 ~ violent_t_lag1 + lmmilex_persol_t + years_since_coup + ucdp_civconf_t2 + defense_target + capprop + jdem + majorpower + cold_war + t_since + t2_since + t3_since'\n",
    "formula7 = 'dispute2 ~ size_t_b_lag1 + lmmilex_persol_t + years_since_coup + ucdp_civconf_t2 + defense_target + capprop + jdem + majorpower + cold_war + t_since + t2_since + t3_since'\n",
    "formula8 = 'dispute2 ~ C(mduration_t_cat3_lag1) + lmmilex_persol_t + years_since_coup + ucdp_civconf_t2 + defense_target + capprop + jdem + majorpower + cold_war + t_since + t2_since + t3_since'\n",
    "\n",
    "# 9) Fit lagged models\n",
    "result5 = smf.logit(formula5, data_lagged).fit(disp=False, cov_type='HC1')\n",
    "result6 = smf.logit(formula6, data_lagged).fit(disp=False, cov_type='HC1')\n",
    "result7 = smf.logit(formula7, data_lagged).fit(disp=False, cov_type='HC1')\n",
    "result8 = smf.logit(formula8, data_lagged).fit(disp=False, cov_type='HC1')\n",
    "\n",
    "# Optional: check names for lagged duration categories\n",
    "me8 = result8.get_margeff(at='mean', method='dydx').summary_frame()\n",
    "print(\"Lagged duration ME index:\", me8.index.tolist())\n",
    "\n",
    "# 10) Collect lagged marginal effects\n",
    "effects_lagged = [\n",
    "    extract_effect(result5, 'mutiny_t_lag1', 'Military Mutiny (t–1)'),\n",
    "    extract_effect(result6, 'violent_t_lag1', 'Violent Mutiny (t–1)'),\n",
    "    extract_effect(result7, 'size_t_b_lag1', 'Large Mutiny (t–1)'),\n",
    "    extract_effect(result8, 'C(mduration_t_cat3_lag1)[T.1.0]', 'Short Mutiny (t–1)'),\n",
    "    extract_effect(result8, 'C(mduration_t_cat3_lag1)[T.2.0]', 'Long Mutiny (t–1)')\n",
    "]\n",
    "\n",
    "me_lagged = pd.DataFrame(effects_lagged)\n",
    "\n",
    "# 11) Plot lagged marginal effects\n",
    "y_positions_lagged = [2.2, 1.6, 1.0, 0.4, -0.2]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ax.set_facecolor('#f5f5f5')\n",
    "\n",
    "ax.errorbar(\n",
    "    x=me_lagged['MarginalEffect'],\n",
    "    y=y_positions_lagged,\n",
    "    xerr=me_lagged['SE'],\n",
    "    fmt='o',\n",
    "    color='black',\n",
    "    ecolor='dimgray',\n",
    "    elinewidth=1.8,\n",
    "    capsize=4,\n",
    "    capthick=1.4,\n",
    "    markersize=6\n",
    ")\n",
    "\n",
    "ax.axvline(0, color='gray', linestyle='--', linewidth=1)\n",
    "ax.set_yticks(y_positions_lagged)\n",
    "ax.set_yticklabels(me_lagged['Variable'])\n",
    "ax.invert_yaxis()\n",
    "\n",
    "ax.set_xlabel(\"Marginal Effect on MID Initiation (Challenger)\", fontsize=11)\n",
    "ax.set_title(\"Mutiny Characteristics (t–1)\", fontsize=12)\n",
    "\n",
    "ax.set_ylim(-0.7, 2.7)\n",
    "ax.set_xlim(\n",
    "    me_lagged['MarginalEffect'].min() - 0.0003,\n",
    "    me_lagged['MarginalEffect'].max() + 0.0005\n",
    ")\n",
    "\n",
    "for spine in ['top', 'right', 'left', 'bottom']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "ax.grid(True, linestyle=':', color='gray', alpha=0.4)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
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